Graphs, Algorithms, and Optimization William Kocay, Donald L. Kreher

Graph theory offers a rich source of problems and techniques for programming and data structure development, as well as for understanding computing theory, including NP-completeness and polynomial reduction. A comprehensive text, Graphs, Algorithms, and Optimization features clear exposition on modern algorithmic graph theory presented in a rigorous, yet approachable way. The book covers major areas of graph theory including discrete optimization and its connection to graph algorithms. The authors explore surface topology for an intuitive point of view and include detailed discussions on linear programming that emphasize graph theory problems useful in mathematics and computer science. Many algorithms are provided along with the data structure needed to program the algorithms efficiently. The book also provides coverage on algorithms complexity and efficiency, NP-completeness, linear optimization, and linear programming and its relationship to graph algorithms. Written in an accessible and informal style, this work covers nearly all areas of graph theory. Graphs, Algorithms, and Optimization provides a modern discussion on graph theory applicable to mathematics, computer science, and crossover applications. Features: · Provides a thorough treatment of graph theory along with data structures to show how algorithms can be programmed. · Includes three chapters on linear optimization, which show how linear programming is related to graph theory. · Emphasizes the use of programming to solve graph theory problems. · Presents all algorithms from a generic point of view, usable with any programming language. William Kocay is a member of the Computer Science Department at the University of Manitoba, Canada. Donald L. Kreher is a professor of Mathematics at the Michigan Technological University, Houghton, USA.